Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Performance comparison of ML models. by Gourab Saha (8987405)

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Evaluation metrics of selected ML models. by Gourab Saha (8987405)

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Example of accuracy plots for soil organic carbon (oc_usda.c729_w.pct) content and soil pH in water (ph.h2o_usda.a268_index) with Lin’s CCC gradually decreasing from MIR to NIR Neo... by José L. Safanelli (20552654)

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Performance Table for ML Models. by Gaurav Sandeep Dave (21519884)

“…The quality of recorded data plays a crucial role and directly influences the data transformation phase in machine learning (ML) and deep learning (DL) models. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Decision curve analysis of PIERS-ML model on day 0 (a), day 4 (b), day 8 (c), and day 13 (d). by Guiyou Yang (10259597)

Decrease in loss during the training process. by Muhammad Idrees (92773)

Loss of MAP4K4 expression causes a decrease in the number of neutrophils. by Guihua Wang (286390)

Crop yield prediction accuracy using different ML algorithms. by Gourab Saha (8987405)

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Table 2_Identify predictive factors for the emergence of self-reported oropharyngeal dysphagia in older men and women populations: a retrospective cohort analysis.xlsx by Shuang Liu (8449)

Table 3_Identify predictive factors for the emergence of self-reported oropharyngeal dysphagia in older men and women populations: a retrospective cohort analysis.xlsx by Shuang Liu (8449)

Table 1_Identify predictive factors for the emergence of self-reported oropharyngeal dysphagia in older men and women populations: a retrospective cohort analysis.xlsx by Shuang Liu (8449)

Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers a discussion of the measures of high interest in clinical implementation. Sensitivity (true positive rate) reflects the ability to correctly identify patients with CVD, while Specificity (true negative rate) indicates the ability to correctly rule out patients without CVD. The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power.... by Medhat A. Tawfeek (22522087)

“…The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power. …”

Supplementary file 1_Harnessing AI for aphasia: a case report on ChatGPT's role in supporting written expression.docx by Avery K. Allen (21449492)

“…While writing aids show promise, artificial intelligence (AI) tools, such as large language models (LLMs), offer new opportunities for individuals with language-based writing challenges.…”

Daily number of adverse outcomes (bars) and area under the receiver operating characteristic curve per day of the PIERS-ML model. by Guiyou Yang (10259597)

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

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